Python netCDF4.Dataset类代码示例

def get_level(resource, level):
  from flyingpigeon.ocgis_module import call
  from netCDF4 import Dataset
  from flyingpigeon.utils import get_variable
  from numpy import squeeze

  try:
    level_data = call(resource, level_range=[int(level),int(level)])
    if type(resource) == list:
      resource.sort()
    variable = get_variable(level_data)
    logger.info('found %s in file' % variable)
    ds = Dataset(level_data, mode='a')
    var = ds.variables.pop(variable)
    dims = var.dimensions
    new_var = ds.createVariable('z%s'% level, var.dtype, dimensions=(dims[0],dims[2],dims[3]))
    # i = where(var[:]==level)
    new_var[:,:,:] = squeeze(var[:,0,:,:])
    ds.close()
    logger.info('level %s extracted' % level)

    data = call(level_data , variable = 'z%s'%level)
    
  except Exception as e:
    logger.error('failed to extract level %s ' % e)
  return data

def read_netcdf(nc_file, variables=None, coords=None):
    """
    Read data from input netCDF. Will read all variables if none provided.
    Will also return all variable attributes.
    Both variables (data and attributes) are returned as dictionaries named
    by variable
    """

    f = Dataset(nc_file, 'r')

    if not variables:
        variables = f.variables.keys()
    if not coords:
        coords = slice(None)

    log.debug('Reading input data variables: %s, from file: %s', variables,
              nc_file)

    d = {}
    a = {}
    g = {}

    for var in variables:
        d[var] = f.variables[var][coords]
        a[var] = f.variables[var].__dict__

    for attr in f.ncattrs():
        g[attr] = getattr(f, attr)

    f.close()

    return d, a, g

 def runTest(self):
     """testing NETCDF3_64BIT_DATA format (CDF-5)"""
     f  = Dataset(self.netcdf_file, 'r')
     assert f.dimensions['dim'].size == dimsize
     assert_array_equal(arrdata, f.variables['var'][:ndim])
     assert (type(f.int64_attr) == np.int64)
     f.close()

    def setUp(self):
        """ Check that a the AIMS system or this script hasn't been modified.
        This function checks that a downloaded file still has the same md5.
        """
        logging_aims()
        channel_id                   = '8365'
        from_date                    = '2008-09-30T00:27:27Z'
        thru_date                    = '2008-09-30T00:30:00Z'
        level_qc                     = 1
        aims_rss_val                 = 100
        xml_url                      = 'http://data.aims.gov.au/gbroosdata/services/rss/netcdf/level%s/%s' % (str(level_qc), str(aims_rss_val))

        aims_xml_info                = parse_aims_xml(xml_url)
        channel_id_info = aims_xml_info[channel_id]
        self.netcdf_tmp_file_path    = download_channel(channel_id, from_date, thru_date, level_qc)
        modify_soop_trv_netcdf(self.netcdf_tmp_file_path, channel_id_info)

        # force values of attributes which change all the time
        netcdf_file_obj              = Dataset(self.netcdf_tmp_file_path, 'a', format='NETCDF4')
        netcdf_file_obj.date_created = "1970-01-01T00:00:00Z"
        netcdf_file_obj.history      = 'data validation test only'
        netcdf_file_obj.close()

        shutil.move(self.netcdf_tmp_file_path, remove_creation_date_from_filename(self.netcdf_tmp_file_path))
        self.netcdf_tmp_file_path    = remove_creation_date_from_filename(self.netcdf_tmp_file_path)

    def test_3d(self):
        """testing variable slicing"""
        f  = Dataset(self.file, 'r')
        v = f.variables['data']
        vu = f.variables['datau']

        # test return of array scalar.
        assert_equal(v[0,0,0].shape,())
        assert_array_equal(v[:], datarev)
        # test reading of slices.
        # negative value means count back from end.
        assert_array_equal(v[:-1,:-2,:-3],datarev[:-1,:-2,:-3])
        # every other element (positive step)
        assert_array_equal(v[2:-1:2,2:-2:2,2:-3:2],datarev[2:-1:2,2:-2:2,2:-3:2])
        # every other element (negative step)
        assert_array_equal(v[-1:2:-2,-2:2:-2,-3:2:-2],datarev[-1:2:-2,-2:2:-2,-3:2:-2])
        # read elements in reverse order
        assert_array_equal(v[:,::-1,:],data)
        assert_array_equal(v[::-1,:,::-1],datarev[::-1,:,::-1])
        assert_array_equal(v[xdim-1::-3,:,zdim-1::-3],datarev[xdim-1::-3,:,zdim-1::-3])

        # ellipsis slice.
        assert_array_equal(v[...,2:],datarev[...,2:])
        # variable with an unlimited dimension.
        assert_array_equal(vu[:], data[:,::-1,:])
        # read data in reverse order
        assert_array_equal(vu[:,::-1,:],data)
        # index using an integer array scalar
        i = NP.ones(1,'i4')[0]
        assert_array_equal(v[i],datarev[1])

        f.close()

class NetCDFData(Data):

    def __init__(self, url):
        self._dataset = None
        self.__timestamp_cache = TTLCache(1, 3600)
        super(NetCDFData, self).__init__(url)

    def __enter__(self):
        self._dataset = Dataset(self.url, 'r')

        return self

    def __exit__(self, exc_type, exc_value, traceback):
        self._dataset.close()

    @property
    def timestamps(self):
        if self.__timestamp_cache.get("timestamps") is None:
            var = None
            for v in ['time', 'time_counter']:
                if v in self._dataset.variables:
                    var = self._dataset.variables[v]
                    break

            t = netcdftime.utime(var.units)
            timestamps = np.array(
                map(
                    lambda ts: t.num2date(ts).replace(tzinfo=pytz.UTC),
                    var[:]
                )
            )
            timestamps.flags.writeable = False
            self.__timestamp_cache["timestamps"] = timestamps

        return self.__timestamp_cache.get("timestamps")

def monreduce(filein):
    ''' Averages the files down to a month.
        
        Creates a new temporary file per month.
    '''
    directory = '/group_workspaces/jasmin/hiresgw/mj07/monthly_means/'
    month = filein[:-7]
    create_temp_nc(month)
    # Get the 3 files for each month
    files = glob(directory+'temp_files/'+filein[-22:-7]+'??.temp.v.nc')
    fileblob = ''
    for filename in files:
        fileblob+=filename+','
    splitind = range(0,1024,128)
    pool = Pool(processes=8)
    TASKS = [(fileblob,n) for n in splitind]
    meansection = [pool.apply_async(splitmonthcalc, t) for t in TASKS]
    mean = np.concatenate((meansection[0].get(),meansection[1].get(),\
                        meansection[2].get(),meansection[3].get(),\
                        meansection[4].get(),meansection[5].get(), \
                        meansection[6].get(),meansection[7].get()), 2)
    print 'done for %s' % (month)    
    # Save mean in file
    filename = directory+'temp_files/'+filein[-22:-7]+'.temp.v.nc'
    f = Dataset(filename,'a')
    u = f.variables['v']
    u[:] = mean[:]
    
    f.close()

def get_indices_from_file(path = 'data/streamflows/hydrosheds_euler9/aex_discharge_1970_01_01_00_00.nc'):
    fpin = Dataset(path)
    vars = fpin.variables

    x, y = vars['x-index'][:], vars['y-index'][:]
    fpin.close()
    return x, y

def open(filename):
    '''Import netCDF output file as OpenDrift object of correct class'''

    import os
    import logging
    import pydoc
    from netCDF4 import Dataset
    if not os.path.exists(filename):
        logging.info('File does not exist, trying to retrieve from URL')
        import urllib
        try:
            urllib.urlretrieve(filename, 'opendrift_tmp.nc')
            filename = 'opendrift_tmp.nc'
        except:
            raise ValueError('%s does not exist' % filename)
    n = Dataset(filename)
    try:
        module_name = n.opendrift_module
        class_name = n.opendrift_class
    except:
        raise ValueError(filename + ' does not contain '
                         'necessary global attributes '
                         'opendrift_module and opendrift_class')
    n.close()

    cls = pydoc.locate(module_name + '.' + class_name)
    if cls is None:
        from models import oceandrift3D
        cls = oceandrift3D.OceanDrift3D
    o = cls()
    o.io_import_file(filename)
    logging.info('Returning ' + str(type(o)) + ' object')
    return o

    def __init__(self, coord_file="coordinates.nc"):
        ds = Dataset(coord_file)
        self.target_lons = ds.variables["glamt"][:]
        self.target_lats = ds.variables["gphit"][:]

        print("target lons shape = ", self.target_lons.shape)
        ds.close()

class FileBuffer(object):
    """ Class that encapsulates and manages deferred access to file data. """

    def __init__(self, filename, dimensions):
        self.filename = filename
        self.dimensions = dimensions  # Dict with dimension keyes for file data
        self.dataset = None

    def __enter__(self):
        self.dataset = Dataset(str(self.filename), 'r', format="NETCDF4")
        return self

    def __exit__(self, type, value, traceback):
        self.dataset.close()

    @property
    def lon(self):
        lon = self.dataset[self.dimensions['lon']]
        return lon[0, :] if len(lon.shape) > 1 else lon[:]

    @property
    def lat(self):
        lat = self.dataset[self.dimensions['lat']]
        return lat[:, 0] if len(lat.shape) > 1 else lat[:]

    @property
    def data(self):
        if len(self.dataset[self.dimensions['data']].shape) == 3:
            return self.dataset[self.dimensions['data']][:, :, :]
        else:
            return self.dataset[self.dimensions['data']][:, 0, :, :]

    @property
    def time(self):
        if self.time_units is not None:
            dt = num2date(self.dataset[self.dimensions['time']][:],
                          self.time_units, self.calendar)
            dt -= num2date(0, self.time_units, self.calendar)
            return list(map(timedelta.total_seconds, dt))
        else:
            return self.dataset[self.dimensions['time']][:]

    @property
    def time_units(self):
        """ Derive time_units if the time dimension has units """
        try:
            return self.dataset[self.dimensions['time']].units
        except:
            try:
                return self.dataset[self.dimensions['time']].Unit
            except:
                return None

    @property
    def calendar(self):
        """ Derive calendar if the time dimension has calendar """
        try:
            return self.dataset[self.dimensions['time']].calendar
        except:
            return 'standard'

def piomasReader(directory,month,years):
    """
    Reads piomas data for sea ice thickness over 1979-2015
    """
    
    ### Enter filename
    filename = 'piomas_regrid_sit_19792015.nc'   
    
    ### Month/Years extracted
    dateyr = now.year  
    datemo = datetime.date(dateyr,month+1,1).strftime('%B')
    
    yearsp = np.arange(1979,2016)
    yearmin = years.min()
    yearmax = years.max()
    yearnone = 2010
    yearslice = np.where((yearsp <= yearmax) & (yearsp >= yearmin) & \
                (yearsp != yearnone))[0]
    
    ### Retrieve data
    data = Dataset(directory + filename)
    latp = data.variables['lat'][:]
    lonp = data.variables['lon'][:]
    thk_p = data.variables['newthickness'][yearslice,month,:,:]
    data.close()
    
    print 'Completed: PIOMAS data read (%s)!' % datemo
    
    return latp,lonp,thk_p

def mooring_2dvar(ncfile, level):
    """Standard EcoFOCI Mooring .nc files with two dimensional parameters as a function of time
    (such as ein - echo intensity
    Timestep of data is assumed to be in fractions of a day"""

    ###nc readin/out
    nchandle = Dataset(ncfile,'r')
    params = ['time', 'time2', 'depth','latitude', 'longitude', 'AGC1_1221']
    time = nchandle.variables[params[0]][:]
    time2 = nchandle.variables[params[1]][:]
    lat = nchandle.variables[params[3]][:]
    lon = nchandle.variables[params[4]][:]
    depth = nchandle.variables[params[2]][:]
    ncdata = nchandle.variables[params[5]][:,:,0,0]
    nchandle.close()
    
    dt = 1. #data is hourly
    time_base = 'hours'
    
    pytime = util.EPICdate2udunits(time, time2)
    
    xx = ncdata[:,level]
    
    dt = 24. * (1. / pytime['interval_min']) #data is 4 times daily
    print dt
    time = pytime['timeint']

    variance = np.var(xx)
    #normalize
    print 'Variance = %s ' % (variance)
    x = (xx - np.mean(xx)) / np.sqrt(variance)
    variance = np.var(x)

    return (ncdata, x,dt,np.array(time) * 24., variance, time_base, depth) 

def set_basic_md(resource):
  """
  basis meta data
  :param resource: netCDF file where basic meta data should be set
  """
  import sys
  from datetime import datetime as dt 
  
  py_version = sys.version
  creation_date = dt.strftime( dt.now(), format='%Y-%m-%dT%H:%M:%S')
  
  md_basic = {
     'activity': 'birdhouse project',
     'software':'flyingpigeon v 0.1', 
     'software_project': 'birdhouse',
     'software_reference':'https://github.com/bird-house/',
     'software_platform': 'PYTHON %s' % py_version,
     'contact_mail_1':'[email protected]',
     'contact_mail_2':'[email protected]',
     'creation_date': creation_date ,
     }
  
  ds = Dataset(resource, mode='a')
  ds.setncatts(md_basic)
  ds.close()
  
  return(resource)

    def ReadFile(self):
        '''
        读取TCCON数据,此数据一个站点一个文件，当站点目录下多个文件只保留一个结果
        '''

        if self.FileList == []:
            return

        for file in self.FileList:
            ncFile = Dataset(file, 'r', format='NETCDF3_CLASSIC')  # 'NCETCDF4'
            ncTime = ncFile.variables['time'][:]
            xco2_ppm = ncFile.variables['xco2_ppm'][:]
            ncFile.close()
            # print file

        for i in xrange(len(ncTime)):
            seconds = ncTime[i] * 24 * 60 * 60
            strTime = time.strftime('%Y-%m-%d %H:%M:%S', time.gmtime(seconds))
            self.FileLine.append([strTime, xco2_ppm[i]])
        # print self.FileLine[0], self.FileLine[-1]

        title = ['time', 'xco2']
        dtype = ['S19'] + ['f4']
        ary = np.core.records.fromarrays(np.array(self.FileLine).transpose(),
            names=','.join(title),
            formats=','.join(dtype))
        condition = np.logical_and(ary['xco2'] > 0, ary['xco2'] < 600)
        self.FileData = ary[np.where(condition)]